System and method for research report guided proactive news analytics for streaming news and social media

ABSTRACT

Systems and methods may provide proactive news analytics based on integrated prediction statements. Users may extract and collect conditional statements from research reports. A compiled list based on the processing/linking of statements for signal generation may then be created. Similarly, a list of counter statements that are assigned a conflict rating that specifies how much agreement/disagreement on a specific topic exists for the counter statement and the statement itself may be created. The custom library may have semantic capabilities to justify conditional statements in order to capture meaning and identify supporting news related to the statement. When a relevant event is detected that relates to a conditional statement, expected conclusions are linked, and customized indexes are calculated to allow for analysis of the relevant event.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/839,061, filed Jun. 25, 2013. The contents of which are herebyincorporated by reference in their entirety.

The application is related to co-pending U.S. patent application Ser.No. 13/972,388, filed on Aug. 21, 2013, and entitled “System and Methodfor Customized Sentiment Signal Generation through Machine LearningBased Text Analytics.” The contents which are hereby incorporated byreference in their entirety.

BACKGROUND

There are many sources for news and information available today. Thesheer volume of sources and information available, both in print andon-line, can be overwhelming. The advent of social media has added anentirely new layer to sources of information. Even more troublesome canbe monitoring these sources to find important information, or at leastinformation important to the reader or the reader's industry. Typicalanalysis tools do not always find relevant material and are notcustomized to a particular user.

Research reports typically incorporate conditional statements such as:if market growth in a sector stays below 2% by Q2 2013, company X stockis expected to be $abc or as new patent law changes are likely to beeffective in Q3 2013, company Y stock can see $xyz by Q4. Suchpredictions may be difficult to process for a variety of reasons, suchas the statements not directly sentiment related, they are not realtime, and they can affect a range of entities. However, by the time theevent mentioned happens, the competitive advantage diminishes.Furthermore, these statements are hard to be considered after a periodof time after publication of the report.

These and other deficiencies exist.

SUMMARY OF THE PREFERRED EMBODIMENTS

An exemplary embodiment includes a computer implemented method forextracting one or more statements from one or more unstructured sourceswherein the extracted statements comprise predictive and conditionalstatements pertaining to an event in the future; normalizing, by thecomputer processor, the extracted statements; linking the extractedstatements from the one or more unstructured sources based on one ormore factors; testing the extracted statements for consistency, whereininconsistent statements are removed; and creating, by the computerprocessor, a list of custom keywords based on the extracted statements.

The computer implemented method may further include associating asentiment position signal with each of the extracted statements;creating a custom keyword library and natural language processingstructures based on each sentiment position signal; linking the customkeyword library and the natural language processing structures based ona semantic analysis; prefetching data from a stream of news, socialmedia, and unstructured sources wherein the data is prefetched based onpotential matching of the data to items in the custom keyword libraryand the natural language processing structures; analyzing the prefetcheddata based on the custom keyword library and the natural languageprocessing structures for matching items; processing the matching itemsbased upon sentiment and confidence scoring; and creating a customizedoutput.

Another exemplary embodiment includes a computer implemented method foracquiring a plurality of statements from multiple input channels,wherein each of the plurality of statements relates to an event that ispredicted to occur in the future and each of the multiple input channelshas an inherent confidence score based on historical performance of eachof the multiple input channels relating to statements and predictedevents; processing each of the plurality of statements, wherein theprocessing comprises one or more of: linking data structures with timestamps for action, assigning importance, and delineating consequences;normalizing each of the plurality of statements to account fordifferences in structure and format of the multiple input channels;linking and compiling each of the plurality of statements through one ormore of hierarchy, timeline, entity connectivity, and causalityimplications; verifying each of the plurality of statements forconsistency in a multi-dimensional space; removing inconsistentstatements that fall below an established threshold for consistency;calculating a unique confidence score based on a matching between two ormore of the plurality of statements; forming a new group of statementsbased on remaining statements, wherein the new group of statements isweighted based on the inherent confidence score; creating a customkeyword library based on the new group of statements; creating naturallanguage processing structures for statement justification; associatingsentiment signals with the new group of statements; linking the customkeyword library and the natural language processing structures;prefetching signals from a stream comprising one or more sourcescomprising news, social media, and research reports; checking eachprefetched signal for a potential match to the custom keyword libraryand the natural language processing structures, wherein upon adetermination of a match the potential match becomes a matched signal;and performing a confidence check on each matched signal based on theweighting based on the inherent confidence score.

Another exemplary embodiment includes a computer implemented method foracquiring a plurality of statements related to a specific entity frommultiple input channels, wherein each of the plurality of statementsrelates to an event that is predicted to occur in the future and each ofthe multiple input channels has an inherent confidence score based onhistorical performance associated therewith; processing each of theplurality of statements, wherein the processing comprises one or moreof: linking data structures with time stamps for action, assigningimportance, and delineating consequences; normalizing each of theplurality of statements to account for differences in structure andformat of the multiple input channels; linking and compiling each of theplurality of statements through one or more of hierarchy, timeline,entity connectivity, and causality implications; verifying each of theplurality of statements for consistency in a multi-dimensional space;removing inconsistent statements falling below an established thresholdof consistency; calculating a unique confidence score based on amatching between two or more of the plurality of statements; forming anew group of statements based on remaining statements; creating a customkeyword library based on the new group of statements; creating naturallanguage processing structures for statement justification; associatingsentiment signals with the new group of statements; linking the customkeyword library and the natural language processing structures;prefetching signals from a stream comprising one or more sourcescomprising news, social media, and research reports; weighting eachprefetched signal based on the inherent confidence score; checking eachprefetched signal for a potential match to the custom keyword libraryand the natural language processing structures, wherein upon adetermination of a match the potential match becomes a matched signal;performing a confidence check comprising assignment of a confidencescore on each matched signal based on the weighting; and combining eachmatched signal to create a position signal for the specific entity usingthe weighting.

Another exemplary embodiment includes a computer implemented method foracquiring a plurality of statements from multiple input channels,wherein each of the plurality of statements relates to an event that ispredicted to occur in the future and each of the multiple input channelshas an inherent confidence score based on historical performanceassociated therewith; processing each of the plurality of statements,wherein the processing comprises one or more of: linking data structureswith time stamps for action, assigning importance, and delineatingconsequences; normalizing each of the plurality of statements to accountfor differences in structure and format of the multiple input channels;linking and compiling each of the plurality of statements through one ormore of hierarchy, timeline, entity connectivity, and causalityimplications; verifying each of the plurality of statements forconsistency in a multi-dimensional space; calculating a uniqueconfidence score based on a matching between two or more of theplurality of statements; removing inconsistent statements based on anestablished threshold, wherein statements below the threshold areremoved; forming a new group of statements based on remaining statementsfollowing the removing; creating a custom keyword library based on thenew group of statements; creating natural language processing structuresfor statement justification; associating sentiment signals with the newgroup of statements; linking the custom keyword library and the naturallanguage processing structures; prefetching signals from a streamcomprising one or more sources comprising news, social media, andresearch reports; checking each prefetched signal for a potential matchto the custom keyword library and the natural language processingstructures, wherein upon a determination of a match the potential matchbecomes a matched signal; applying machine learning based on eachmatched signal, wherein the machine learning comprises identifyingindicators in the matched signal to identify additional signals topre-fetch; and prefetching, from the stream one or more additionalsignals based on the machine learning.

In exemplary embodiments, the preceding methods may be performed using asystem with a processor and a memory comprising computer-readableinstructions which when executed by the processor cause the processor toperform the method steps.

These and other embodiments and advantages of the preferred embodimentswill become apparent from the following detailed description, taken inconjunction with the accompanying drawings, illustrating by way ofexample the principles of the various exemplary embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a system in accordance with an exemplary embodiment.

FIG. 2 is an example of research report position statements andprefetching news associated with statements according to an exemplaryembodiment.

FIGS. 3A and 3B are flow charts of a method for proactive news analyticson integrated prediction statements in accordance with an exemplaryembodiment.

FIG. 4 is an example diagram of supporting statements according toexemplary embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

It will be readily understood by those persons skilled in the art thatthe embodiments of the inventions described herein are capable of broadutility and application.

Accordingly, while the invention is described herein in detail inrelation to the exemplary embodiments, it is to be understood that thisdisclosure is illustrative and exemplary of embodiments are described toprovide an enabling disclosure. Accordingly, the disclosure is notintended to be construed to limit the embodiments or otherwise toexclude any other such embodiments, adaptations, variations,modifications and equivalent arrangements.

The following descriptions are provided of different configurations andfeatures according to exemplary embodiments. These configurations andfeatures may relate to providing financial services to customers. Whilecertain nomenclature and types of applications/hardware are described,other names and application/hardware usage is possible and thenomenclature provided is done so by way of non-limiting examples only.Further while particular embodiments are described, it should beappreciated that the features and functions of each embodiment may becombined in any combination as is within the capability of one ofordinary skill in the art. The attached Figures provide additionaldetails of the various embodiments. It should also be appreciated thatthese exemplary embodiments are provided as non-limiting examples only.

Exemplary embodiments may be applicable to financial institutions.Accordingly, examples used herein may refer to financial institutions ortopics related thereto. Financial institutions, as used herein, mayinclude institutions that provide financial services to their members orcustomers. For example, financial institutions may include, but are notlimited to banks, credit unions, trust companies, mortgage loancompanies, insurance companies, investment banks, underwriters, andbrokerage firms. It should be appreciated that the exemplary embodimentmay be extended to other industries and areas beyond financialinstitutions. Therefore, the examples used herein are exemplary andmeant to be non-limiting.

According to exemplary embodiments, proactive news analytics may beproduced based on integrated prediction statements. As described in thebackground above, many unstructured sources, such as, but not limitedto, research reports, typically incorporate conditional statements. Totake advantage of these conditional statements exemplary embodiments mayallow users to extract and collect such conditional statements from theresearch reports. The statements may be extracted and collected eitherdirectly by the analysts, or automatically by text analytics, orextracted by the user through text tear outs. Following this process, anextracted statements flat list may be created and then used forproactive news processing. Statements from multiple reports fromdifferent sources and different levels of the hierarchy (such asstatement on a large geographic area that affects predictions oncompanies in a particular country in that geographic area) may bemerged.

Thus, a compiled list based on the processing/linking of statements forsignal generation is created and maintained. Similarly maintained may bea list of counter statements that are assigned a conflict rating thatspecifies how much agreement/disagreement on a specific topic exists forthe counter statement and the statement itself. A custom library forstreaming news may then be maintained. The custom library may not bepurely keyword based but may have semantic capabilities to justifyconditional statements in order to capture meaning and identifysupporting news related to the statement. When a relevant event isdetected that relates to a conditional statement, expected conclusionsare linked, and customized indexes are calculated to allow for analysisof the relevant event. The goal being to proactive be aware of therelevant event rather than waiting for the news to be pushed andreacting. Instead, according to exemplary embodiments, it is possible toget in front of the events to act accordingly to take advantage of thepredicted event based on the conditional statement.

The exemplary methods and systems provided herein are provided by way ofexample, as there are a variety of ways to carry out the methodsdisclosed herein. The methods as shown in the Figures may be executed orotherwise performed by one or a combination of various systems asdescribed herein. Each block shown in the Figures may represent one ormore processes, methods, and/or subroutines carried out in the exemplarymethod. Each block may have an associated processing machine or theblocks depicted may be carried out through one processor machine.Furthermore, while the steps may be shown in a particular order, itshould be appreciated that the steps may be conducted in a differentorder.

While a single illustrative block, module or component is shown, theseillustrative blocks, modules or components may be multiplied for variousapplications or different application environments. In addition, themodules or components may be further combined into a consolidated unit.The modules and/or components may be further duplicated, combined and/orseparated across multiple systems at local and/or remote locations. Forexample, some of the modules or functionality associated with themodules may be supported by a separate application or platform. Otherimplementations and architectures may be realized. It should beappreciated that exemplary systems may be integrated into and run on acomputer, which may include a programmed processing machine having oneor more processors. Such a processing machine may execute instructionsstored in a memory to process the data and execute the methods describedherein. Furthermore, exemplary systems may be integrated into and run onone or more computer networks which may each have one or more computersassociated therewith. Exemplary systems may be cloud computing typesystems.

As noted above, the processing machine executes the instructions thatare stored in the memory or memories or persistent or non-transitorydata storage devices to process data. This processing of data may be inresponse to commands by a user or users of the processing machine, inresponse to previous processing, in response to a request by anotherprocessing machine and/or any other input, for example. As describedherein, a module performing functionality may have a processor.

FIG. 1 is a system according to exemplary embodiments. System 100 mayprovide various functionality and features associated with the program.More specifically, system 100 may include a device 110, a second device120, and an Nth device 130, a network 135, a processing module 140, adatabase 150, other systems 160, and a server 170.

According to exemplary embodiments, the system 100 may be configured tocarry out the methods as described herein. The system 100 may havedevice 110 associated therewith. A second device 120 and an Nth device130 may be further associated with the system 100. The devices 110, 120,and 130 may each be a processing machine. Each device 110, 120, and 130may include software and/or modules to implement the methods describedherein according to exemplary embodiments. Each device 110, 120, and 130may provide processing, display, storage, communications, and executionof commands in response to inputs from a user thereof and respond torequests from the software and/or modules. It should be appreciated thateven though the devices 110, 120, and 130 may be referred to in thesubsequent description, the system 100 may use any combination of thesedevices ranging from one device 110 to multiple devices 110, 120, and130.

The devices 110, 120, and 130 may each serve as a client side. Eachdevice 110, 120, and 130 may be a “fat” client, such that the majorityof the processing may be performed on the client. Alternatively, thedevice 110, 120, and 130 may each be a “thin” client, such that themajority of the processing may be performed in the other components ofthe system 100. The devices 110, 120, and 130 may be configured toperform other functions and processing beyond the methods describedherein. The devices 110, 120, and 130 may each be a part of a largersystem. The devices 110, 120, and 130 may be multi-functional inoperation. The devices 110, 120, and 130 may each support the operationand running of one or more applications or programs.

Each device 110, 120, and 130 may have a display and an input deviceassociated therewith. The display may be monochrome or color. Forexample, the display may be a plasma, liquid crystal, or cathode raytube type display. The displays may be touch screen type displays. Thedevices 110, 120, and 130 may have more than one display. The multipledisplays may be different types of displays. The display may havesub-displays there on. For example, the device 110, 120 and 130 may havea large display surface. The display for the user interface may occupy aportion or less than the whole of the large display surface.

The input device may be a single device or a combination of inputdevices. For example, the input devices may include a keyboard, bothfull-sized QWERTY and condensed, a numeric pad, an alpha-numeric pad, atrack ball, a touch pad, a mouse, selection buttons, and/or a touchscreen. As described above, the display may serve as an input devicethrough using or incorporating a touch screen interface. The devices110, 120, and 130 may include other devices such as a printer and adevice for accepting deposits and/or dispensing currency and coins.

The device 110, 120, and 130 may have one or more cameras, opticalsensors, or other sensing devices. The sensors may be computercontrolled and may capture digital images.

According to some embodiments, the devices 110, 120, and 130 may beportable electronic devices or mobile electronic devices. The user mayinteract with the portable electronic device through various inputdevices (not shown). For example, the portable electronic device mayhave a display screen to convey information to the user. The display maybe a color display. For example, the display may be a Liquid CrystalDisplay (“LCD”). The portable electronic device may have one or moreinput devices associated with it. For example, the portable electronicdevice may have an alpha-numeric keyboard, either physical or virtual,for receiving input. The portable electronic device may have a QWERTYstyle keyboard, either physical or virtual. The portable electronicdevice may have a pointing device associated therewith, such as, forexample, a trackball or track wheel. The portable electronic device mayreceive inputs through a touch screen or other contact interface. Insome embodiments, gesture based input may be used. A combination ofinput types may be used. As described above, the portable electronicdevice may have communication capabilities over both cellular andwireless type networks to transmit/receive data and/or voicecommunications.

The portable electronic device, by way of non-limiting examples, mayinclude such portable computing and communications devices as mobilephones (e.g., cell or cellular phones), smart phones (e.g., iPhones,Android based phones, or Blackberry devices), personal digitalassistants (PDAs) (e.g., Palm devices), laptops, netbooks, tablets, orother portable computing devices. These portable electronic devices maycommunicate and/or transmit/receive data over a wireless signal. Thewireless signal may consist of Bluetooth, Wireless Application Protocol(WAP), Multimedia Messaging Service (MMS), Enhanced Messaging Service(EMS), Short Message Service (SMS), Global System for MobileCommunications (GSM) based systems, Code Division Multiple Access (CDMA)based systems, Transmission Control Protocol/Internet (TCP/IP)Protocols, or other protocols and/or systems suitable for transmittingand receiving data from the portable electronic device. The portableelectronic device may use standard wireless protocols which may includeIEEE 802.11a, 802.11b, 802.11g, and 802.11n. Such portable electronicdevices may be Global Positioning System (GPS) capable. GPS is asatellite based system which sends a signal allowing a device to defineits approximate position in a coordinate system on the earth. That is,the portable electronic device may receive satellite positioning dataand display the location on the earth of the portable electronic deviceusing GPS. Other location systems may be used. The portable electronicdevice may include one or more computer processors and be capable ofbeing programmed to execute certain tasks.

The device 110, 120, and 130 may establish communications with otherparts of the system 100 over a network 135. Upon successful initiationof communications between the and the network 135 and another part ofthe system 100, such as, for example, processing module 140 and database150, data may be exchanged between device 110, 120, and 130 and theparticular element over the network 135. Data may be transmitted fromdevice 110, 120, and 130. Data may be transmitted from the other part ofthe system 100 to the device 110, 120, and 130.

The devices 110, 120, and 130 may be communicatively coupled to thenetwork 135. Accordingly, the devices 110, 120, and 130 may begeographically dispersed. Conversely, two or more of devices 110, 120,and 130 may be located in close proximity. For example, the devices maybe located within or near an office complex. Wherever the location ofthe device 110, 120, and 130, it may still be able to communicativelycouple with the network 135 and the components of the system 100.

In some embodiments, the devices 110, 120, and 130 may be other types ofcomputing platforms, such as, for example, a desktop computer or alaptop computer. The devices 110, 120, and 130 may be a combination ofcomputing devices.

The devices 110, 120, and 130 may each be remotely accessible. Remoteaccess may be used to configure, troubleshoot, and wipe the contents ofthe device, for example.

Network 135 may be a computer based network, with one or more serversand/or computer processors. For example, network 135 may be the Internetor a network connected to the Internet. The network 135 may be asatellite or cellular based network. Information and data may beexchanged through the network 135 between the various components of thesystem 100. In alternative embodiments, the network 135 may be a localarea network within the financial institution that may be connected toor interface with the Internet. It should be appreciated that thenetwork 135 may be a combination of local area networks, wide areanetworks, and external networks, which may be connected to the Internet.

The processing module 140 may be communicatively coupled to the network135. The processing module 140 may perform operations associated withthe establishment, configuration, and application of the programsaccordingly to exemplary embodiments. The processing module 140 mayconsist of one or more servers and/or general purpose computers, eachhaving one or more computer processors associated therewith.

The processing module 140 may have a database 150 communicativelycoupled thereto. The database 150 may contain data and information usedby the system 100. For example, the database 150 may store thecustomized library's and other data structures described here.Additional information may be contained therein related to the operationand administration of the system 100.

The database 150 may include any suitable data structure to maintain theinformation and allow access and retrieval of the information. Forexample, the database may keep the data in an organized fashion. Thedatabase 150 may be a database, such as an Oracle database, a MicrosoftSQL Server database, a DB2 database, a MySQL database, a Sybasedatabase, an object oriented database, a hierarchical database, a flatdatabase, and/or another type of database as may be known in the artthat may be used to store and organize rule data as described herein.

The database 150 may be stored in any suitable storage device. Thestorage device may include multiple data storage devices. The multipledata storage devices may be operatively associated with the database150. The storage may be local, remote, or a combination thereof withrespect to the database. The database 150 may utilize a redundant arrayof disks (RAID), striped disks, hot spare disks, tape, disk, or othercomputer accessible storage. In one or more embodiments, the storage maybe a storage area network (SAN), an internet small computer systemsinterface (iSCSI) SAN, a Fiber Channel SAN, a common Internet FileSystem (CIFS), network attached storage (NAS), or a network file system(NFS). The database may have back-up capability built-in. Communicationswith the database 150 may be over a network, such as the network 135, orcommunications may be over a direct connection between the database 150and the processing module 140, as depicted in FIG. 1. Data may betransmitted and/or received from the database 150. Data transmission andreceipt may utilize cabled network or telecom connections such as anEthernet RJ 15/Category 5 Ethernet connection, a fiber connection, atraditional phone wireline connection, a cable connection or other wirednetwork connection. A wireless network may be used for the transmissionand receipt of data.

The system 100 may have other systems 160 associated therewith. Theseother systems 160 may include various data collection and supportsystems used by the entity to carry out a variety of functions. Theother systems 160 may include equipment and other assets of the entity.The other systems 160 may be associated with third party entities.

It should be appreciated that the server 170 may interact with otherparts of the system 100, such as the devices 110, 120, and 130, as wellas the processing module 140 and the other systems 160. The server 170may be a single server or it may be multiple servers. For example, theserver 170 may represent multiple servers located in differentlocations. The server 170 may be a part of a cloud computing system. Theserver 170 may server a variety of roles in the system 100. In someembodiments, the server 170 may contain the processing module 140 aswell as the database 150. In some embodiments, the database 150 may bedirectly coupled to the server 170.

The server 170 may have one or more storage devices associatedtherewith. The storage may be local, remote, or a combination thereofwith respect to the server 170. The storage may utilize a redundantarray of disks (RAID), striped disks, hot spare disks, tape, disk, orother computer accessible storage. In one or more embodiments, thestorage may be a storage area network (SAN), an Internet small computersystems interface (iSCSI) SAN, a Fiber Channel SAN, a common InternetFile System (CIFS), network attached storage (NAS), or a network filesystem (NFS). The storage may have back-up capability built-in. Theback-up capability of the storage may be used to archive image data forlater use. The back-up capability may be used for recovery of data inthe event of a failure of the storage.

In some embodiments, the server 170 may be associated with one or morethird party entities whereas other portions of the system 100 (e.g.,device 110, 120, and 130, processing module 140, database 150, and othersystems 160) may be associated with a single entity according toexemplary embodiments, such as, for example, a financial institution.

FIG. 2 is an example of research report position statements andprefetching news associated with statements according to an exemplaryembodiment. It should be appreciated that FIG. 2 is but one embodiment,as this figure can have multiple embodiments. For example, the plot 200may represent the interconnection between a prefetched news article anda series of statements and events. In some embodiments, the news articlemay lead to the statements and events depicted (that is, the eventdescribed in the news article may lead to the statements and events).This embodiment may be used for machine learning following theoccurrence of an event to track the result of the event and to learn thekeywords, expressions, and sentiment that resulted in an effort tobetter predict events in the future. In another embodiment, the plot maybe of expected events and the event's connectivity may be specified by aresearch report or analyst. In a different embodiment, a compilation ofextracted sentences can be produce this corresponding sequence ofevents, streaming data and interconnectivity information.

FIGS. 3A and 3B are flow charts of a method for proactive news analyticson integrated prediction statements according to exemplary embodimentsof the invention.

At block 302, statement extraction from unstructured sources isperformed. For example, the sources may include research reports. Thestatement extraction may include analyst or externally provided readilyavailable statements, statements extracted by the user (e.g. tear outsfrom unstructured text), automatically extracted statements from varietyof sources, and user generated statements.

In one embodiment, position statements may be extracted from a researchreport for a specific equity. Such statements may include expectedbehavior of the stock, expected events directly related to the stock,expected events in the market, expected events relating to competitors,prediction statements, etc. For example, if the stock is downgraded, thestatements may include things like “expected retirement of the CEO”,“indications of slow-down in the segment”, “expected product release bya competitor”, etc. Each of these statements may have different timestamps and confidence levels in the future. Such statements maytypically be found in the abstract section of a position report or inother sections and different types of reports and documents.

In another embodiment, analysts may provide flow charts that describe ingreater detail the justification for position and associated weights forstatements. Such statements can be extracted and input to the systemthrough a variety of ways, including but not limited to: (i) textanalytics based extraction of statements from research reports, (ii)analyst provided statements (explicitly provided by analyst), (iii) usertear outs from research reports and other sources, and (iv) usergenerated expectation or prediction statements.

In another embodiment, analysts may provide quantitative models, flowcharts, or predictive statements, or a combination of these items toguide the decision process. Such data may be machine readable and can beused to guide the ingestion and prefetching of streaming data sources.

At block 304, first stage processing of the extracted statements isperformed. This may include categorization of the extracted statements.As part of this processing, linking data structures with time stamps foraction, importance, and consequences is performed. In one embodiment,custom weights may be assigned by analysts. In another embodiment, acustom weight may be calculated depending on the confidence level forthe statement and the analyst.

At block 306, normalization of the extracted statements from differentsources is performed within each category from block 304. In oneembodiment, for example, the user may extract statements from variousresearch reports from different investment banks or various analystswithin the investment bank or research institution, from news sources,institution reports, social media and financial blog statements. As eachof these sources have different format and structural characteristics,data from the different sources is processed to be normalized. Suchnormalization also includes normalization of expected time frames,currencies, market and financial metrics, etc.

At block 308, linking and compiling of the extracted statements isperformed. The linking and compiling may be performed through hierarchy,timeline, entity connectivity, and causality implications. In oneembodiment, the hierarchy may refer to a market segment and specificequities in the segment. In another embodiment, a timeline may connectmonths to quarters. Entity connectivity may reflect direct competitorsin a specific segment, etc.

At block 310, the extracted statements are checked for consistency.Consistency may be checked in multi-dimensional space of time,causality, and hierarchy. In one embodiment, a unique confidence scoremay be calculated when statements from different sources match. Thisconfidence score may then be used to prefetch news and supporting datain the later parts of the method. In cases where the statements have lowconsistency scores, two embodiments may exist: in one embodiment, theinconsistent statements may be pruned such that highest confidencestatement or source remains; in the second embodiment, separatestatement and position flows are generated and both inconsistentstatements are tracked for prefetching.

At block 312, if the statements are not consistent, the statements areremoved. A threshold may be set for consistency. The statements may beremoved if they fall below the threshold. Such thresholds may be machinelearned for the industry/segment and for specified risk levels. Thethresholds can also be overwritten by the user.

At block 314, a new group of statements is formed. The new group may beformed if the statements are above the threshold.

At block 316, a custom keyword library is created. The custom librarymay be created for pre-fetching of streaming news, social media, andpre-news for statement. In one embodiment, each word in normalized and acompiled statement list may be processed, analog words/statements may becreated and processed similarly. In another embodiment, one such librarycould contain “company name”, “lawsuit”, “patent”, “litigation”, etc. Inanother embodiment, specific names (such as person, place, product, etc)can be associated with the statements and placed in the keyword library.

The custom keyword library may have a hierarchy such that each statementhas its own keyword, expression, or natural language processingstructures to track. The composite library may have a combination of thecompiled statements such the structures may overlap (e.g., keyword,expression, timeline, natural language processing structures, etc.).

In another embodiment, quantitative support engines provided by analystsor other sources can be used to guide the prefetching process by usingthe causal relationships. If a causal relationship is identified betweena news/event and a statement such filters can be used to do prefetching.In other embodiments, such prefetching sources can be machine learnedpost-event in the first instance and can be applied to other instances.

At block 318, natural language processing (NLP) structures for statementjustification are created. These NLP structures may be created forstreaming news, social media, and pre-news for statement. The NLPstructures may serve to provide a way to identify analogous expressionsfor the contents of the custom keyword library. The NLP structures mayserve to provide a way to identify analogous expressions for thecontents of the custom keyword library. In this step, the relationshipsbetween some of the keywords may be further defined and new relationshipconstructs may be added.

At block 320, sentiment signals are associated with the statementssupported by the news. In one embodiment, the sentiment signal may bemultiplied by the factor to determine the impact on position statementas described in FIG. 4. The sentiment or position signal may also bemultiplied by the confidence score of the channel.

At block 322, the customized library and NLP structures are linked. Thelinking may be performed based on semantic analysis of the event to besupported with statement predications, such as expected timelines andconfidence scores.

At block 324, weighted stream items are pre-fetched and scanned. Thestream items may include streaming news, social media, research reportsfrom the same institution or others, institutional reports, andunstructured sources. The stream items may include streaming news,social media, and unstructured sources. The pre-fetching is doneproactively with justification engines based on the custom keywordlibrary and structures created above. The pre-fetching enables exemplaryembodiments to find and identify precursor events and other items thatindicate the predicted event may occur. In this way, the predicted eventmay be anticipated. For example, many times events and other items mayoccur and be reported as a fact that the event occurred. The actual newsstory may happen later, such as a day or two afterwards or a result ofthe event may occur. The goal may be to find the precursors and othertells that are indicative of the actual event.

Each source in the stream may be weighted. The weighting may be done bya user based on preferences. In some embodiments, each source may begiven equal weight. In other embodiments, different sources may beweighted differently. The weighting may be applied during the processingfor confidence as described below. In another embodiment, the weightsmay be dynamically updated by the machine learning algorithm based onthe historical data.

At block 326, the potential match of the items is individually checked.The potential match may be checked with the structure and libraryengines. A match may be greater than a specified threshold.

At block 328, if the item matches, then the item is processed. Aconfidence check may be performed and a multi-channel scan may be doneto identify supporting items. The weighting of the source may be appliedas part of the confidence check to increase or decrease the confidencefor each matching item.

If the item does not match, it is discarded and other items are fetchedand scanned.

At block 330, spoofing checks are performed and alerts generated. Thespoofing checks may be performed by requesting confirmation throughreported news sources and independent channels.

At block 332, custom signals are created from matching items and thevisualization for the user is updated. Reporting of the emergingpre-news and updated signals along with the associated sentiment may beperformed.

At block 334, machine learning is implemented. The method 300 mayimplement machine learning to pre-fetch particular signals for futuretracking. For each event with an impact greater than a predeterminedthreshold, the method may identify indicators, such as other events,new, and/or keywords that may be used as part of the learning toidentify particular items to pre-fetch.

In another embodiment, the system may learn from high impact events byretroactively scanning the data to find sources for prefetching andother early indicators. Statistical analysis of the data may beperformed to identify the custom library associated with the event,different sources of data for prefetching of the news and other sources.All of these entries may be stored in a database that is dynamicallyupdated based on observed events and is used to retrain the system.

FIG. 4 is an example diagram of supporting statements according toexemplary embodiments. FIG. 4 is meant to be exemplary and non-limiting.The diagram 400 depicts the interrelationship of a tear out of astatement from a research report that is linked with the supportingstatements from other sources, such as news/data/references. Eachsupporting statement has a sentiment and confidence score assigned asdescribed herein. Each supporting statement is sentiment scored andlinked to its source. The diagram is also dynamically updated.

Hereinafter, aspects of implementation of the inventions will bedescribed. As described above, the method of the invention may becomputer implemented as a system. The system of the invention orportions of the system of the invention may be in the form of a“processing machine,” for example. As used herein, the term “processingmachine” is to be understood to include at least one processor that usesat least one memory. The at least one memory stores a set ofinstructions. The instructions may be either permanently or temporarilystored in the memory or memories of the processing machine. Theprocessor executes the instructions that are stored in the memory ormemories in order to process data. The set of instructions may includevarious instructions that perform a particular task or tasks, such asthose tasks described above in the flowcharts. Such a set ofinstructions for performing a particular task may be characterized as aprogram, software program, or simply software.

The description of exemplary embodiments describes servers, portableelectronic devices, and other computing devices that may include one ormore modules, some of which are explicitly depicted in the figures,others are not. As used herein, the term “module” may be understood torefer to executable software, firmware, hardware, and/or variouscombinations thereof. It is noted that the modules are exemplary. Themodules may be combined, integrated, separated, and/or duplicated tosupport various applications. Also, a function described herein as beingperformed at a particular module may be performed at one or more othermodules and/or by one or more other devices (e.g., servers) instead ofor in addition to the function performed at the particular module.Further, the modules may be implemented across multiple devices and/orother components local or remote to one another. Additionally, themodules may be moved from one device and added to another device, and/ormay be included in both devices. It is further noted that the softwaredescribed herein may be tangibly embodied in one or more physical media,such as, but not limited to, a compact disc (CD), a digital versatiledisc (DVD), a floppy disk, a hard drive, read only memory (ROM), randomaccess memory (RAM), as well as other physical media capable of storingsoftware, and/or combinations thereof. Moreover, the figures illustratevarious components (e.g., servers, portable electronic devices, clientdevices, computers, etc.) separately. The functions described as beingperformed at various components may be performed at other components,and the various components may be combined and/or separated. Othermodifications also may be made.

According to exemplary embodiments, the systems and methods may becomputer implemented using one or more computers, incorporating computerprocessors. The computer implementation may include a combination ofsoftware and hardware. The computers may communicate over a computerbased network. The computers may have software installed thereonconfigured to execute the methods of the exemplary embodiments. Thesoftware may be in the form of modules designed to cause a computerprocessor to execute specific tasks. The computers may be configuredwith hardware to execute specific tasks. As should be appreciated, avariety of computer based configurations are possible.

The processing machine described above may also utilize any of a widevariety of other technologies including a special purpose computer, acomputer system including a microcomputer, mini-computer or mainframefor example, a programmed microprocessor, a micro-controller, a PICE(peripheral integrated circuit element), a CSIC (Customer SpecificIntegrated Circuit) or ASIC (Application Specific Integrated Circuit) orother integrated circuit, a logic circuit, a digital signal processor, aprogrammable logic device such as a FPGA, PLD, PLA or PAL, or any otherdevice or arrangement of devices for example capable of implementing thesteps of the process of the invention.

It is appreciated that in order to practice the method of the inventionas described above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. For example, each of the processors and the memoriesand the data stores used in the invention may be located ingeographically distinct locations and connected so as to communicate inany suitable manner. Additionally, it is appreciated that each of theprocessor and/or the memory and/or data stores may be composed ofdifferent physical pieces of equipment. Accordingly, it is not necessarythat the processor be one single piece of equipment in one location andthat the memory be another single piece of equipment in anotherlocation. For example, it is contemplated that the processor may be twoor more pieces of equipment in two or more different physical locations.These two or more distinct pieces of equipment may be connected in anysuitable manner. Additionally, the memory may include two or moreportions of memory in two or more physical locations. Additionally, thedata storage may include two or more components or two or more portionsof memory in two or more physical locations.

To explain further, processing as described above is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two distinct components as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by twodistinct components. In a similar manner, the memory storage performedby two distinct memory portions as described above may, in accordancewith a further embodiment of the invention, be performed by a singlememory portion. Further, the memory storage performed by one distinctmemory portion as described above may be performed by two memoryportions. It is also appreciated that the data storage performed by twodistinct components as described above may, in accordance with a furtherembodiment of the invention, be performed by a single component.Further, the data storage performed by one distinct component asdescribed above may be performed by two distinct components.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; e.g., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, such as a computernetwork, for example, the Internet, Intranet, Extranet, LAN, or anyclient server system that provides communication of any capacity orbandwidth, for example. Such communications technologies may use anysuitable protocol such as TCP/IP, UDP, or OSI, for example. It should beappreciated that examples of computer networks used in the precedingdescription of exemplary embodiments, such as the Internet, are meant tobe non-limiting and exemplary in nature.

As described above, a set of instructions is used in the processing ofthe invention. The set of instructions may be in the form of a programor software. The software may be in the form of system software orapplication software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example. Thesoftware used might also include modular programming in the form ofobject oriented programming or any other suitable programming form. Thesoftware tells the processing machine what to do with the data beingprocessed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. For example, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, e.g., to a particular type ofcomputer, for example. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, ActionScript, Ada, APL,Basic, C, C++, C#, COBOL, Ceylon, Dart, dBase, F#, Fantom, Forth,Fortran, Go, Java, Jquery, Modula-2, .NET, Objective C, Opa, Pascal,Prolog, Python, REXX, Ruby, Visual Basic, X10, and/or JavaScript, forexample. Further, it is not necessary that a single type of instructionsor single programming language be utilized in conjunction with theoperation of the system and method of the invention. Rather, any numberof different programming languages may be utilized as is necessary ordesirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module,for example.

As described above, the invention may illustratively be embodied in theform of a processing machine, including a computer or computer system,for example, that includes at least one memory. It is to be appreciatedthat the set of instructions, e.g., the software for example, thatenables the computer operating system to perform the operationsdescribed above may be contained on any of a wide variety of computerreadable media, as desired. Further, the data for example processed bythe set of instructions might also be contained on any of a wide varietyof non-transitory media or medium. For example, the particular medium,e.g., the memory in the processing machine, utilized to hold the set ofinstructions and/or the data used in the invention may take on any of avariety of physical forms or transmissions, for example. Illustratively,the medium may be in the form of paper, paper transparencies, a compactdisk, a DVD, an integrated circuit, a hard disk, a floppy disk, anoptical disk, a magnetic tape, a RAM, a ROM, a PROM, a EPROM, a wire, acable, a fiber, communications channel, a satellite transmissions orother remote transmission, as well as any other medium or source of datathat may be read by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements the invention may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In the system and method of the invention, a variety of “userinterfaces” may be utilized to allow a user to interface with theprocessing machine or machines that are used to implement the invention.As used herein, a user interface includes any hardware, software, orcombination of hardware and software used by the processing machine thatallows a user to interact with the processing machine. A user interfacemay be in the form of a dialogue screen for example. A user interfacemay also include any of a mouse, touch screen, keyboard, voice reader,voice recognizer, dialogue screen, menu box, list, checkbox, toggleswitch, a pushbutton or any other device that allows a user to receiveinformation regarding the operation of the processing machine as itprocesses a set of instructions and/or provide the processing machinewith information. Accordingly, the user interface is any device thatprovides communication between a user and a processing machine. Theinformation provided by the user to the processing machine through theuser interface may be in the form of a command, a selection of data, orsome other input, for example.

As discussed above, a user interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a user. The user interface is typically usedby the processing machine for interacting with a user either to conveyinformation or receive information from the user. However, it should beappreciated that in accordance with some embodiments of the system andmethod of the invention, it is not necessary that a human user actuallyinteract with a user interface used by the processing machine of theinvention. Rather, it is contemplated that the user interface of theinvention might interact, e.g., convey and receive information, withanother processing machine, rather than a human user. Accordingly, theother processing machine might be characterized as a user. Further, itis contemplated that a user interface utilized in the system and methodof the invention may interact partially with another processing machineor processing machines, while also interacting partially with a humanuser.

While the embodiments have been particularly shown and described withinthe framework of financial services, it will be appreciated thatvariations and modifications may be effected by a person of ordinaryskill in the art without departing from the scope of the invention.Furthermore, one of ordinary skill in the art will recognize that suchprocesses and systems do not need to be restricted to the specificembodiments described herein. Other embodiments, combinations of thepresent embodiments, and uses and advantages of the present inventionwill be apparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. Thespecification and examples should be considered exemplary.

What is claimed is:
 1. A computer implemented method, comprising:extracting, by a computer processor, one or more statements from one ormore unstructured sources wherein the extracted statements comprisepredictive and conditional statements pertaining to an event in thefuture; normalizing, by the computer processor, the extractedstatements; linking the extracted statements from the one or moreunstructured sources based on one or more factors; testing the extractedstatements for consistency, wherein inconsistent statements are removed;creating, by the computer processor, a list of custom keywords based onthe extracted statements; ranking the extracted statements based on astrength of agreement; and creating an alternative conditional statementflow chart upon the extracted statements having a high degree ofdisagreement.
 2. The method of claim 1, further comprising: associatinga sentiment position signal with each of the extracted statements;creating a custom keyword library and natural language processingstructures based on each sentiment position signal; linking the customkeyword library and the natural language processing structures based ona semantic analysis; prefetching data from a stream of news, socialmedia, and unstructured sources wherein the data is prefetched based onpotential matching of the data to items in the custom keyword libraryand the natural language processing structures; analyzing the prefetcheddata based on the custom keyword library and the natural languageprocessing structures for matching items; processing the matching itemsbased upon sentiment and confidence scoring; and creating a customizedoutput.
 3. The method of claim 2, wherein the potential matching of thedata to items in the custom keyword library and the natural languageprocessing structures is measured at least in terms comprising one ormore of: entity matching, keyword matching, and timeline matching. 4.The method of claim 2, further comprising: verifying the matching itemsusing a set of cross-channel checks that comprise verifying eachmatching item against one or more other independent sources.
 5. Themethod of claim 2, wherein the stream is a weighted stream with weightsapplied to each source in the stream.
 6. The method of claim 2, furthercomprising: implementing machine based learning for future prefetchingbased on an accuracy of the matching items and the event.
 7. The methodof claim 2, further comprising: applying a feedback mechanism thatextracts information from the one or more unstructured sourcespertaining to the occurrence of the event.
 8. The method of claim 1,wherein the unstructured sources comprise research reports.
 9. Themethod of claim 1, wherein the one or more factors comprise one or moreof: the conditions or predictions that the extracted statements referto, an expected timeline, an expected cause, an expected dependency, akeyword or keywords, and a list of tracked entities.
 10. The method ofclaim 1, further comprising: creating a linked and timeline based flowdiagram based on the extracted statements and the event.
 11. A system,comprising: a processor; and a memory comprising computer-readableinstructions which when executed by the processor cause the processor toperform the steps comprising: extracting one or more statements from oneor more unstructured sources wherein the extracted statements comprisepredictive and conditional statements pertaining to an event in thefuture; normalizing the extracted statements; linking the extractedstatements from the one or more unstructured sources based on one ormore factors; testing the extracted statements for consistency, whereininconsistent statements are removed; creating a list of custom keywordsbased on the extracted statements; ranking the extracted statementsbased on a strength of agreement; and creating an alternativeconditional statement flow chart if the extracted statements have a highdegree of disagreement.
 12. The system of claim 11, further comprising:associating a sentiment position signal with each of the extractedstatements; creating a custom keyword library and natural languageprocessing structures based on each sentiment position signal; linkingthe custom keyword library and the natural language processingstructures based on a semantic analysis; prefetching data from a streamof news, social media, and unstructured sources wherein the data isprefetched based on potential matching of the data to items in thecustom keyword library and the natural language processing structures;analyzing the prefetched data based on the custom keyword library andthe natural language processing structures for matching items;processing the matching items based upon sentiment and confidencescoring; and creating a customized output.
 13. The system of claim 12,further comprising: verifying the matching items using a set ofcross-channel checks that comprise verifying each matching item againstone or more other independent sources.
 14. The system of claim 12,wherein the stream is a weighted stream with weights applied to eachsource in the stream.
 15. The system of claim 12, further comprising:implementing machine based learning for future prefetching based on anaccuracy of the matching items and the event.
 16. The system of claim12, further comprising: applying a feedback mechanism that extractsinformation from the one or more unstructured sources pertaining to theoccurrence of the event.
 17. The system of claim 11, wherein theunstructured sources comprise research reports.
 18. The system of claim11, wherein the one or more factors comprise one or more of: theconditions or predictions that the extracted statements refer to, anexpected timeline, an expected cause, an expected dependency, a keywordor keywords, and a list of tracked entities.
 19. The system of claim 11,further comprising: creating a linked and timeline based flow diagramfor the extracted statements and the event.
 20. A computer implementedmethod, comprising: acquiring, by a computer processor, a plurality ofstatements from multiple input channels, wherein each of the pluralityof statements relates to an event that is predicted to occur in thefuture and each of the multiple input channels has an inherentconfidence score based on historical performance of each of the multipleinput channels relating to statements and predicted events; processing,by the computer processor, each of the plurality of statements, whereinthe processing comprises one or more of: linking data structures withtime stamps for action, assigning importance, and delineatingconsequences; normalizing each of the plurality of statements to accountfor differences in structure and format of the multiple input channels;linking and compiling, by the computer processor, each of the pluralityof statements through one or more of hierarchy, timeline, entityconnectivity, and causality implications; verifying each of theplurality of statements for consistency in a multi-dimensional space;removing inconsistent statements that fall below an establishedthreshold for consistency; calculating, by the computer processor, aunique confidence score based on a matching between two or more of theplurality of statements; forming a new group of statements based onremaining statements, wherein the new group of statements is weightedbased on the inherent confidence score; creating, by the computerprocessor, a custom keyword library based on the new group ofstatements; creating, by the computer processor, natural languageprocessing structures for statement justification; associating sentimentsignals with the new group of statements; linking, by the computerprocessor, the custom keyword library and the natural languageprocessing structures; prefetching signals, by the computer processor,from a stream comprising one or more sources comprising news, socialmedia, and research reports; checking, by the computer processor, eachprefetched signal for a potential match to the custom keyword libraryand the natural language processing structures, wherein upon adetermination of a match the potential match becomes a matched signal;and performing, by the computer processor, a confidence check on eachmatched signal based on the weighting based on the inherent confidencescore.
 21. The computer implemented method of claim 20, wherein themultiple input channels comprise: user inputs, text tear outs fromdocuments, automatically extracted statements from documents, newssources, and inputs from individual analysts.
 22. The computerimplemented method of claim 21, wherein the inputs from individualanalysts comprise machine readable data comprising one or more of flowcharts, quantitative models, predictive statements, or a combinationthereof.
 23. The computer implemented method of claim 20, wherein themulti-dimensional space comprises time, causality, and hierarchy. 24.The computer implemented method of claim 20, further comprising:normalizing each word in the new group of statements; and compiling astatement list with analog words and statements based on the new groupof statements; and adding the statement list to the custom keywordlibrary.
 25. The computer implemented method of claim 20, wherein thenatural language processing structures identify analogous expressions tocontents of the custom keyword library.
 26. The computer implementedmethod of claim 20, further comprising: retroactively scanning thestream, after the event has occurred, for signals related to occurrenceof the event; processing, by a computer processor, the signals relatedto the occurrence; performing, by the computer processor, a statisticalanalysis of the signals; identifying a custom library of keywords fromthe statistical analysis; storing the custom library of keywords fromthe statistical analysis; implementing machine learning based on thecustom library of keywords and the occurrence to improve futureprefetching of signals.
 27. A computer implemented method, comprising:acquiring, by a computer processor, a plurality of statements related toa specific entity from multiple input channels, wherein each of theplurality of statements relates to an event that is predicted to occurin the future and each of the multiple input channels has an inherentconfidence score based on historical performance associated therewith;processing, by the computer processor, each of the plurality ofstatements, wherein the processing comprises one or more of: linkingdata structures with time stamps for action, assigning importance, anddelineating consequences; normalizing each of the plurality ofstatements to account for differences in structure and format of themultiple input channels; linking and compiling, by the computerprocessor, each of the plurality of statements through one or more ofhierarchy, timeline, entity connectivity, and causality implications;verifying each of the plurality of statements for consistency in amulti-dimensional space; removing inconsistent statements falling belowan established threshold of consistency; calculating, by the computerprocessor, a unique confidence score based on a matching between two ormore of the plurality of statements; forming a new group of statementsbased on remaining statements; creating, by the computer processor, acustom keyword library based on the new group of statements; creating,by the computer processor, natural language processing structures forstatement justification; associating sentiment signals with the newgroup of statements; linking, by the computer processor, the customkeyword library and the natural language processing structures;prefetching signals, by the computer processor, from a stream comprisingone or more sources comprising news, social media, and research reports;weighting each prefetched signal based on the inherent confidence score;checking, by the computer processor, each prefetched signal for apotential match to the custom keyword library and the natural languageprocessing structures, wherein upon a determination of a match thepotential match becomes a matched signal; performing, by the computerprocessor, a confidence check comprising assignment of a confidencescore on each matched signal based on the weighting; and combining eachmatched signal to create a position signal for the specific entity usingthe weighting.
 28. The computer implemented method of claim 27, whereinthe multiple input channels comprise: user inputs, text tear outs fromdocuments, automatically extracted statements from documents, newssources, and inputs from individual analysts.
 29. The computerimplemented method of claim 27, wherein the multi-dimensional spacecomprises time, causality, and hierarchy.
 30. A computer implementedmethod, comprising: acquiring, by a computer processor, a plurality ofstatements from multiple input channels, wherein each of the pluralityof statements relates to an event that is predicted to occur in thefuture and each of the multiple input channels has an inherentconfidence score based on historical performance associated therewith;processing, by the computer processor, each of the plurality ofstatements, wherein the processing comprises one or more of: linkingdata structures with time stamps for action, assigning importance, anddelineating consequences; normalizing each of the plurality ofstatements to account for differences in structure and format of themultiple input channels; linking and compiling, by the computerprocessor, each of the plurality of statements through one or more ofhierarchy, timeline, entity connectivity, and causality implications;verifying each of the plurality of statements for consistency in amulti-dimensional space; calculating, by the computer processor, aunique confidence score based on a matching between two or more of theplurality of statements; removing inconsistent statements based on anestablished threshold, wherein statements below the threshold areremoved; forming a new group of statements based on remaining statementsfollowing the removing; creating, by the computer processor, a customkeyword library based on the new group of statements; creating, by thecomputer processor, natural language processing structures for statementjustification; associating sentiment signals with the new group ofstatements; linking, by the computer processor, the custom keywordlibrary and the natural language processing structures; prefetchingsignals, by the computer processor, from a stream comprising one or moresources comprising news, social media, and research reports; checking,by the computer processor, each prefetched signal for a potential matchto the custom keyword library and the natural language processingstructures, wherein upon a determination of a match the potential matchbecomes a matched signal; applying, by the computer processor, machinelearning based on each matched signal, wherein the machine learningcomprises identifying indicators in the matched signal to identifyadditional signals to pre-fetch; and prefetching, from the stream, bythe computer processor, one or more additional signals based on themachine learning.
 31. The computer implemented method of claim 30,wherein the multiple input channels comprise: user inputs, text tearouts from documents, automatically extracted statements from documents,news sources, and inputs from individual analysts.
 32. The computerimplemented method of claim 30, wherein the multi-dimensional spacecomprises time, causality, and hierarchy.